Technical Field
The present invention relates to risk factor identification, and more particularly to identifying group-level and individual-level risk factors via risk-driven patient stratification.
Description of the Related Art
As more clinical information with increasing diversity becomes available for analysis, a large number of features can be constructed and leveraged for predictive modeling. The ability to identify risk factors related to an adverse health condition (e.g., congestive heart failure) is very important for improving healthcare quality and reducing cost. The identification of risk factors may allow for the early detection of the onset of diseases so that aggressive intervention may be taken to slow or prevent costly and potentially life threatening conditions.
In personalized care management scenarios, it is common for two patients or groups of patients to have similar risk scores, but based on different risk factors. Conventionally, risk factor identification utilizes feature ranking methods to rank features that characterize the global utility of features. However, methods based on general population data will only yield common risk factors and do not address individual differences of patients.